import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np

# 确保每次随机数生成的结果相同
tf.set_random_seed(1)
np.random.seed(1)

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)  # 55000x784

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
lr = 0.0001
step = 2000


# 定义权重
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


# 定义偏置
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


# 定义卷积
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


# 定义池化
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')


# 网络结构
# 1. 第一层卷积： 28x28x1 --> 14x14x32
x_image = tf.reshape(x, [-1, 28, 28, 1])
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
# 2. 第二层卷积： 14x14x32 --> 7x7x64
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
# 3. 全连接层： 7x7x64 --> 1024
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# 4. dropout防过拟合
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# 5. 输出层： 1024 --> 10类
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 6. 评估和训练模型
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy)
# train_step = tf.train.GradientDescentOptimizer(lr).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(step):
        batch = mnist.train.next_batch(100)
        _, train_accuracy, train_loss = sess.run([train_step, accuracy, cross_entropy],
                                                feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
        # 打印结果
        if (i + 1) % 100 == 0:
            print("step %d, training accuracy %f, loss %f" %
                  (i + 1, train_accuracy, train_loss))

    test_accuracy = sess.run(accuracy,
                             feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
    print("test accuracy %f" % test_accuracy)